Adobe Commerce Optimizer, aimed at the experience layer

Adobe has detailed Commerce Optimizer: machine-learned recommendations for the experience layer that improve performance without a replatform.

Adobe Commerce Optimizer, aimed at the experience layer

Following the ACCS announcement on March 19, 2025, Adobe set out where Commerce Optimizer fits in the product suite. The short version: performance improvements without abandoning your current Adobe Commerce infrastructure or funding a replatform to get them.

The choice merchants used to have

When a store hit a performance or capability ceiling, there were two roads. Build your way out with a development project, or replatform. Both cost real money, take real time and carry real risk, and neither is a decision you make in a quarter. Commerce Optimizer breaks that pair.

How it works

Commerce Optimizer uses machine learning to analyse how your store behaves, identify conversion bottlenecks and recommend improvements with evidence attached. It isn't generic advice about page weight — it learns your patterns and ranks its recommendations by likely revenue impact. Optimisation stops being a matter of opinion.

What merchants are reporting

Better conversion rates, faster page loads, clearer inventory visibility and higher customer lifetime value. None of it requires code changes: you configure the recommendations inside the platform. Which means a team without dedicated developers can implement work that used to need them.

Why the experience layer

Commerce Optimizer targets product pages, category browsing, checkout flows and customer journeys — the parts of the store the customer touches. Infrastructure optimisation makes a store more reliable; experience layer optimisation makes it earn more. Both matter, but only one shows up in the revenue line directly.

What this means for you

If you're on Adobe Commerce, this is a route to modern performance without a replatform on the roadmap. It also changes the conversation we have with clients: less migration planning, more sitting down together over which recommendations to take first and how to measure what they did.